Shape Sensing of Flexible Robots Based on Deep Learning

In this article, a deep learning method for the shape sensing of continuum robots based on multicore fiber bragg grating (FBG) fiber is introduced. The proposed method, based on an artificial neural network (ANN), differs from traditional approaches, where accurate shape reconstruction requires a tedious characterization of many...

Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning

Accurate and complete terrain maps enhance the awareness of autonomous robots and enable safe and optimal path planning. Rocks and topography often create occlusions and lead to missing elevation information in the Digital Elevation Map (DEM). Currently, these occluded areas are either fully avoided during motion planning or the missing...

Improving Pedestrian Prediction Models with Self-Supervised Continual Learning

Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This paper introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online across various scenarios continuously. In...

Hiding Assistive Robots During Training in Immersive VR Does Not Affect Users’ Motivation, Presence, Embodiment, Performance, Nor Visual Attention

Combining immersive virtual reality (VR) using head-mounted displays (HMDs) with assisting robotic devices might be a promising procedure to enhance neurorehabilitation. However, it is still an open question how immersive virtual environments (VE) should be designed when interacting with rehabilitation robots. In conventional training, the...

Where to go next: Learning a Subgoal Recommendation Policy for Navigation in Dynamic Environments

Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing. Local trajectory optimization methods, such as model predictive control (MPC), can deal with those changes but require...

Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning

Visual navigation is essential for many applications in robotics, from manipulation, through mobile robotics to automated driving. Deep reinforcement learning (DRL) provides an elegant map-free approach integrating image processing, localization, and planning in one module, which can be trained and therefore optimized for a given environment....

Selecting Informative Data Samples for Model Learning Through Symbolic Regression

Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy is impaired when data from repetitive motions prevail in the training set and outweigh...